Optimal Training System

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-05-C-0015
Agency Tracking Number: F045-014-0247
Amount: $99,946.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF04-T014
Solicitation Number: N/A
Timeline
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2004-12-20
Award End Date (Contract End Date): 2005-09-20
Small Business Information
600 Blvd. South, Suite 104, Huntsville, AL, 35802
DUNS: 174448498
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Ruby Lathon
 Vice President
 (505) 550-7829
 ruby.lathon@emt-inc.com
Business Contact
 Donald Taylor
Title: President
Phone: (256) 705-3502
Email: donald.taylor@emt-inc.com
Research Institution
 CALIFORNIA STATE UNIV.,HAYWARD
 Victoria Jensen
 25800 Carlos Bee Blvd
Hayward, CA, 94542
 (510) 885-2205
 Nonprofit college or university
Abstract
The ability to train novice operators on a variety of tasks through automated training systems provides a cost effective and timely solution for a number of applications. The training regimen required for military personnel lends itself to automated training systems. In order to overcome some of the challenges and shortfalls of the traditional intelligent training systems, a strong normative or expert model must be developed. Such a model provides a foundation by which comparison, monitoring and feedback can take place. EMT, Inc. and California State University, Hayward propose to develop a methodology that brings together both the normative model and the student model in a approach that enables individualized training based on the trainee's individual skills. This objective will be achieved by using machine learning techniques used to develop and maintain an expert learning transition contained in the student model module via two steps. First, machine learning techniques will be used to develop and maintain an expert learning transition in the student model. Second, methods for externalizing the student model will be used to provide for student-system interaction that can be compared against the normative model.

* Information listed above is at the time of submission. *

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